Court System AI Deployment: A 60-Day Pilot Framework

This article walks through a 60-day pilot framework for state court systems considering AI deployment, based on observed 2024-2025 pilot cycles.

Phase 1 (Days 1-10): Setup

• Define specific use case
• Identify pilot users (5-10 lawyers + paralegals)
• Set up BAA-protected LLM endpoint (Azure OpenAI or AWS Bedrock)
• Build RAG corpus (state statutes, local rules)
• Configure audit logging

Phase 2 (Days 11-25): Internal testing

• Lawyers test on historical cases (with known outcomes)
• Track citation accuracy
• Track factual accuracy
• Identify failure patterns

Eval targets: citation accuracy >95%, factual accuracy >95%, latency p95 <2s.

Phase 3 (Days 26-40): Pilot deployment

• Deploy to identified pilot users
• Daily standup for issues
• Weekly eval retest
• Track user satisfaction

Phase 4 (Days 41-55): Pilot evaluation

• Sample 100 queries for lawyer review
• Statistical analysis of accuracy
• User survey
• Edge case inventory

Phase 5 (Days 56-60): Decision

• Met exit criteria: extend or scale
• Failed: end or pivot to alternative

Pilot success criteria

Required for scale:

• Citation accuracy >95%
• Lawyer satisfaction >3.5/5
• No major security incidents
• Documented eval framework
• Audit log integrity

Common pitfalls

• Skipping the eval framework
• Using consumer-tier AI (BAA issues)
• Not involving lawyers in eval design
• Insufficient training of pilot users
• Lack of audit log retention plan

Budget guidance

For a 60-day pilot with 5-10 users:

• LLM API costs: $200-1,000
• Engineer time: $10K-25K
• Lawyer time: $5K-10K
• Total: ~$15K-35K

Acknowledgments

This article summarizes observed pilot cycles as of early 2026.

Dillon Deutsch has worked with state courts on AI pilot design. https://courtgpt.ai